Overview

The data revealed that of the data collected, coral bleaching began in 1973. Data collection and ended in 2011. 50% of the bleached coral data was collected between 1998 and 2004. The average year for coral with a bleached severity code greater than 0 is 2001. The average severity code of the data is 0.8992.

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library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0      ✔ purrr   0.3.5 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.3      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(dplyr)
library(tidyr)
library(readr)
library(knitr)
coral_data <- read_csv("https://raw.githubusercontent.com/info201b-au2022/project-group-28/main/data/CoralBleaching.csv")
## Rows: 6190 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (19): REGION, SUBREGION, COUNTRY, LOCATION, DEPTH, BLEACHING_SEVERITY, C...
## dbl  (9): ID, LAT, LON, MONTH, YEAR, SEVERITY_CODE, MORTALITY_CODE, RECOVERY...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
coral_table <- coral_data %>% 
  select(COUNTRY, CORAL_SPECIES, BLEACHING_SEVERITY, WATER_TEMPERATURE, OTHER_FACTORS) %>% 
  group_by(COUNTRY) %>% 
  filter(CORAL_SPECIES == "Acropora"| CORAL_SPECIES == "Montipora"|
           CORAL_SPECIES == "Pocillopora"| CORAL_SPECIES == "Porites", na.rm = TRUE) %>% 
head(coral_table, n = 54)
kable(coral_table)
>>>>>>> 88485599372a812fc915fdbc93f370c525be727b
COUNTRY CORAL_SPECIES BLEACHING_SEVERITY WATER_TEMPERATURE OTHER_FACTORS
Tanzania Acropora HIGH 3 to 5C above normal NA
Comoros Acropora HIGH NA NA
Comoros Acropora HIGH NA NA
Madagascar Acropora Medium NA NA
Mauritius Acropora Medium NA Cyclone Anacelle produced wet & clooudy weather in Feb.
Réunion Acropora Low NA NA
Réunion Acropora HIGH 1.5c above mean NA
Réunion Pocillopora Medium NA NA
Mexico Pocillopora Medium 30c NA
Mexico Pocillopora Medium 30c NA
Costa Rica Porites HIGH NA NA
Costa Rica Pocillopora Medium NA NA
Japan Acropora HIGH NA NA
Japan Acropora HIGH NA NA
Chagos Archipelago (UK) Acropora HIGH NA NA
India Acropora HIGH NA NA
India Acropora HIGH NA NA
India Montipora HIGH NA NA
Sri Lanka Acropora HIGH NA NA
Sri Lanka Acropora HIGH NA NA
Malaysia Porites Low NA NA
Thailand Acropora Low NA NA
Thailand Acropora HIGH NA NA
Australia Acropora Low NA NA
Australia Acropora HIGH 27-29C NA
Australia Acropora HIGH 27-29C NA
Australia Acropora Severity Unknown NA NA
Australia Acropora Severity Unknown NA NA
Australia Acropora Low NA NA
Australia Acropora HIGH NA NA
Torres Strait & Great Barrier Reef Acropora HIGH NA NA
United Arab Emirates Acropora HIGH 34c NA
United Arab Emirates Acropora HIGH 34c NA
United Arab Emirates Acropora HIGH 2C above average, maximum recorded 35C, anomality start in April and lasted till Sept. NA
United Arab Emirates Acropora Low 2C above average, maximum recorded 35C, anomality start in April and lasted till Sept. coral disease reduced
American Samoa Porites Low NA NA
Fiji Acropora HIGH 30-31c NA
Fiji Acropora HIGH NA NA
Fiji Acropora Medium peaked at 30-30.5oC NA
Fiji Acropora HIGH 31-32c NA
Fiji Acropora Low 30.5-31C NA
Fiji Acropora Low peaked at 30-30.5oC NA
Fiji Acropora HIGH peaked at 30-30.5oC NA
Fiji Acropora HIGH peaked at 30-30.5oC NA
Fiji Pocillopora Medium peaked at 30-30.5oC NA

This table shows the necessary elements and value we used to create our visualizations and answer our research questions. The table includes the information on four main species of corals. It includes the location, bleaching level, and factors to the corals’ bleaching.

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Chart 1

The chart I included displays what kind of coral species are most ======= 

#Chart 1
library(tidyverse)
library(dplyr)
library(tidyr)
library("ggplot2") 

name = c("Acropora", "Montipora", "Pocillopora", "Porites")
Reef_Size = c("small (< 10 cm)", "medium (10-50 cm)", "large (> 50 cm)") 
value = c(56, 12, 5, 3, 50, 22, 11, 1, 55, 12, 48, 0)
corals <- data.frame(name, Reef_Size, value, stringsAsFactors = FALSE) 
ggplot(data = corals, aes(x = name, y = value, fill = Reef_Size)) + 
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Coral Species vs Bleaching Susceptibility", 
       x = "Coral Species", 
       y = "Bleaching Susceptibilty (percentage)")

The chart I included displays what kind of coral species are most >>>>>>> 88485599372a812fc915fdbc93f370c525be727b susceptible to coral bleaching. This bar graph shows four of the most common coral species and their resistance to bleaching based on their coral reef size. We can see that the most susceptible species corals are Acropora and the most resistant are the Porites species.

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Chart 2

======= 
#Chart 2
library(tidyverse)
library(sf)
## Linking to GEOS 3.10.2, GDAL 3.4.2, PROJ 8.2.1; sf_use_s2() is TRUE
library(mapview)
library(readr)
coral_data <- read_csv("https://raw.githubusercontent.com/info201b-au2022/project-group-28/main/data/CoralBleaching.csv")
## Rows: 6190 Columns: 28
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (19): REGION, SUBREGION, COUNTRY, LOCATION, DEPTH, BLEACHING_SEVERITY, C...
## dbl  (9): ID, LAT, LON, MONTH, YEAR, SEVERITY_CODE, MORTALITY_CODE, RECOVERY...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
filtered_coral_data <- filter(coral_data, SEVERITY_CODE > 0)
mapview(filtered_coral_data, xcol = "LON", ycol = "LAT", crs = 4269, 
        grid = FALSE)
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>>>>>>> 037da52f444e0a0dddae5ecd803789334c274478 >>>>>>> 88485599372a812fc915fdbc93f370c525be727b

This map shows the longitude and latitude of all the locations where coral bleaching has a severity code greater than 0 in this data set. As the map shows, coral bleaching affects coral reefs all over the world. It is not just concentrated to one area. The data points appear to be more concentrated north of Australia and between North America and South America. This is also where a larger number of coral reefs are located. This is helpful to understand that coral bleaching does not necessarily have any correlation with a particular region of the world.

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Chart 3

This data visualization shows a general timeline of severe coral ======= 

#Chart 3
library(tidyverse)
library("ggplot2")

severe_coral_bleaching_events <- read.csv("https://raw.githubusercontent.com/info201b-au2022/project-group-28/main/data/coral-bleaching-events.csv")

ggplot(severe_coral_bleaching_events, aes(x=Year, y=Severe.bleaching.events...30..bleached., color=Entity)) +
  geom_point() +
  labs(title = "Severe Coral Bleaching Events Over Time",
       x = "Year",
       y = "Number of Severe Bleaching Events >30% Bleached")

This data visualization shows a general timeline of severe coral >>>>>>> 88485599372a812fc915fdbc93f370c525be727b bleaching events from the year 1930 to 2016, and is organized by general location, which in this case is the ocean’s region. The x-axis shows the change in time, while the y-axis shows the number of severe bleaching events that occurred that year. The data points are organized by location, with a different color representing each region. Overall, Australasia has the most frequent severe coral bleaching events with the exception of the World category.